basic sequence model training

This commit is contained in:
Mike J Innes 2016-10-29 00:10:27 +01:00
parent d9abb8f0ce
commit 4de16171db

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@ -19,7 +19,7 @@ function tf(model::Flux.Unrolled)
Model(model, sess, params,
[instates..., input], [outstates..., output],
[gradients(output, input)]),
[])
batchone.(model.states))
end
function batchseq(xs)
@ -40,3 +40,24 @@ function (m::SeqModel)(x::BatchSeq)
end
(m::SeqModel)(x::Seq) = first(m(batchone(x)))
function Flux.train!(m::SeqModel, train; epoch = 1, η = 0.1,
loss = (y, y) -> reduce_sum((y - y).^2)/2,
opt = TensorFlow.train.GradientDescentOptimizer(η))
i = 0
Y = placeholder(Float32)
Loss = loss(m.m.output[end], Y)
minimize_op = TensorFlow.train.minimize(opt, Loss)
for e in 1:epoch
info("Epoch $e\n")
@progress for (x, y) in train
y, cur_loss, _ = run(m.m.session, vcat(m.m.output[end], Loss, minimize_op),
merge(Dict(m.m.inputs[end]=>batchone(x), Y=>batchone(y)),
Dict(zip(m.m.inputs[1:end-1], m.state))))
if i % 5000 == 0
@show y
end
i += 1
end
end
end